Zeineb Lassoued's research while affiliated with University of Gabès and other places
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Publications (22)
In this paper, a new robust model predictive control (RMPC) is proposed for uncertain nonlinear systems. The nonlinear behavior is described by uncertain piecewise affine models, where the parametric uncertainties are considered time varying with norm-bounded structure. The proposed control scheme consists of two steps. First, a proportional gain o...
This paper concerns the identification of a transesterification reactor using PWARX (PieceWise AutoRegressive eXogenous) hybrid systems. The OBE (Outer Bounding Ellipsoid) algorithm is then applied in order to estimate the parameters for each sub-system. This algorithm is used for system identification when bounded disturbances are present. This pa...
In this paper, the problem of hybrid model predictive control (HMPC) strategy based on fuzzy supervisor for piecewise autoregressive with exogenous input (PWARX) models is addressed. We first represent the nonlinear behavior of the system with a PWARX model. Then, we transform the obtained PWARX model into a mixed logical dynamic (MLD) model in ord...
In this paper, we consider the problems of nonlinear system representation and control. In fact, we propose a solution based on PieceWise Auto-Regressive eXogenous (PWARX) models since these models are able to approximate any nonlinear behaviour with arbitrary precision. Moreover, the identification and control approaches of linear systems can be e...
Dans ce travail, on s’intéresse à la stratégie de commande prédictive MPC (Model Predictive Control) appliquée aux systèmes hybrides. C’est une méthode qui a connu un grand succès pour la commande des procédés industriels et largement appliquée avec des dynamiques linéaires ou non linéaires [55, 100]. Sa popularité est liée à la capacité d’exiger d...
This paper deals with the problem of nonlinear systems control. In fact, we propose an alternative solution based on Piecewise AutoRegressive with eXogenous input (PWARX)
model, Hybrid Model Predictive Control (HMPC) strategy and fuzzy supervisor. The contribution consists in introducing a fuzzy supervisor allowing the online readjustment of the HM...
In this paper, a nonlinear hybrid system predictive control approach is developped. Hybrid process identification is based on PieceWise AutoRegressive eXogenous (PWARX) models. The identificaion problem deals with Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. Model Predictive Control (MPC) strategy is based on Mixe...
This chapter addresses the problem of clustering based procedure for the identification of PieceWise Auto-Regressive eXogenous (PWARX) models. In order to overcome the main drawbacks of the existing methods such as their sensitivity to poor initializations and the existence of outliers, we propose the use of the Chiu’s clustering algorithm and the...
The paper deals with set-membership parameter estimation of
fractional models in the time-domain. In such a context, the
equation-error is supposed to be unknown-but-bounded with a
priori known bounds. The proposed approach is based on the
Optimal Bounding Ellipsoid algorithm and it is applied to linear
fractional systems. Two groups of algorithms...
In this paper, we address the problem of identifying a semi-batch olive oil esterification reactor. In fact, this reactor can be considered as a PieceWise AutoRegressive eXogenous (PWARX) system. The Chiu’s clustering procedure for the identification of PWARX systems is then applied. It consists in estimating both the parameter vector of each submo...
In this paper, we address the problem of identifying a semi-batch olive oil esterification reactor. In fact, this reactor can be considered as a PieceWise AutoRegressive eXogenous (PWARX) system. The Chiu's clustering procedure for the identification of PWARX systems is then applied. It consists in estimating both the parameter vector of each submo...
This paper deals with the problem of piecewise auto regressive systems with exogenous input (PWARX) model identification based on clustering solution. This problem involves both the estimation of the parameters of the affine sub-models and the hyper planes defining the partitions of the state-input regression. The existing identification methods pr...
Among hybrid systems, the piecewise affine systems are a common class to be identified from input/output data. The work presented in this paper is concerned with the identification of piecewise affine systems using clustering based procedures. In fact, the Kohonen's Self Organizing Map is used to identify both the parameters of the affine sub-model...
Among hybrid systems, the piecewise affine systems are a common class to be identified from input/output data. The work presented in this paper is concerned with the identification of piecewise affine systems using clustering based procedures. In fact, the Kohonen's Self Organizing Map is used to identify both the parameters of the affine sub-model...
In this paper the problem of identifying PieceWise AutoRegressive eXogenous (PWARX) systems is treated. Only the clustering based methods are considered. It consists in estimating both the parameter vector of each sub-model and the coefficients of each partition while knowing the model orders and the number of sub-models. We compare the k-means bas...
This paper addresses the problem of clustering-based procedure for the identification of PWARX models. It consists in estimating both the parameter vector of each submodel and the coefficients of each partition. It exploits three main techniques which are clustering, linear identification and pattern recognition. The performance of this approach de...
This paper addresses the problem of clustering-based procedure for the identification of PWARX models. It consists in estimating both the parameter vector of each submodel and the coefficients of each partition. It exploits three main techniques which are clustering, linear identification and pattern recognition. The performance of this approach de...
In this paper, the problem of clustering based procedure for the identification of PieceWise Auto-Regressive eXogenous (PWARX) models is addressed. This problem involves both the estimation of the parameters of the affine sub-models and the hyperplanes defining the partitions of the state-input regression. In fact, we propose the use of the Chiu's...
We consider the clustering-based procedures for the identification of discrete-time hybrid systems in the piecewise affine (PWA) form. These methods exploit three main techniques which are clustering, linear identification, and pattern recognition. The clustering method based on the k-means algorithm is treated in this paper. It consists in estimat...
This paper presents a new ellipsoidal set-membership method for the identification of linear fractional orders systems. It use the Optimal Bounding Ellipsoid (OBE) algorithm. When the probability distribution of the disturbances is unknown but bounded and when the differentiation orders are known, the proposed method can estimate all the feasible p...
Citations
... However, for most real processes, there is some state that cannot be measured. Therefore, to estimate the unmeasured state, researchers propose the design of the observers [40], [41]. In [41], the unknown input hybrid observers of the system (1) is as given by: ...
![[object Object]](https://i1.rgstatic.net/ii/profile.image/273551332737030-1442231128694_Q64/Kamel-Abderrahim.jpg)
... For each one, an ARX model is identified using least squares. After that, similar ARX models are assembled into clusters by the use of a convenient clustering technique Lassoued and Abderrahim (2019), Lassoued and Abderrahim (2014a). Finally, the obtained clusters are delimited with hyperplanes using the Supprot Vector Machine (SVM) approach Wang (2005). ...
... The existing sample data evidently testifies the nonlinearity of Pichia pastoris fermentation. To facilitate the PWA modeling, the Pichia pastoris fermentation (Multi-input Multi-output Nonlinear System) was decomposed into several multiple input single output (MISO) discrete time systems, each of which can be described as a piecewise auto-regressive exogenous (PWARX) model [39]: ...
... In the second one, both coefficients and fractional orders of the system are estimated by minimizing the quadratic criterion based on the output error. For an overview refers to [1] [2] [3][4] [5]. These methods are developed in open-loop conditions. ...
![[object Object]](https://i1.rgstatic.net/ii/profile.image/415734497857536-1476130236302_Q64/Messaoud-Amairi.jpg)
![[object Object]](https://i1.rgstatic.net/ii/profile.image/589698590339072-1517606508168_Q64/Abdelkrim-Mohamed-Naceur.jpg)
... In fact, numerous methods have been proposed in the literature for the identification of PWARX models [10,[32][33][34][35] . These approaches are based on different categories of solutions such as clustering-based solutions [32] , Bayesian solution [36] , bounded-error solution [33] , greedy solution [35] , sparse optimization solution [34] , and so forth. Only the clustering solution is considered since it is based on a simple and instructive procedure. ...
... The application of various filters to separate these aliasing or redundant data points becomes a straightforward task. This research could shed even more light on the anomaly patterns in the area that was researched [2], [3], [30], [38], [45], [46], [47], [48]. ...
... Also, if the number of subsystems is unknown, more complexity is added to the problem. In the hybrid system identification field, the algebraic approach [9], Bayesian approach [10], bounded-error approach [11], mixed-integer programming [12], and the clustering technique [13][14][15][16][17] are the most important approaches, which will be enlightened in the following. For a complete overview of the presented methods and their comparison, one can refer to [1,18,19]. ...
... Our choice is explained by the simplicity of this approach based on instructive procedure. However, the efficiency of the classification algorithm manage the performance of the identification method [12], [13], [14], [15]. ...
... Our choice is explained by the simplicity of this approach based on instructive procedure. However, the efficiency of the classification algorithm manage the performance of the identification method [12], [13], [14], [15]. ...
... For each one, an ARX model is identified using least squares. After that, similar ARX models are assembled into clusters by the use of a convenient clustering technique Lassoued and Abderrahim (2019), Lassoued and Abderrahim (2014a). Finally, the obtained clusters are delimited with hyperplanes using the Supprot Vector Machine (SVM) approach Wang (2005). ...
